Affine transformation analysis system and method for image matching

Abstract

An affine transformation analysis system and method is provided for matching two images. The novel systolic array image affine transformation analysis system comprising a linear rf-processing means, an affine parameter incremental updating means, and a least square error fitting means is based on a Lie transformation group model of cortical visual motion and stereo processing. Image data is provided to a plurality of component linear rf-processing means each comprising a Gabor receptive field, a dynamical Gabor receptive field, and six Lie germs. The Gabor coefficients of images and affine Lie derivatives are extracted from responses of linear receptive fields, respectively. The differences and affine Lie-derivatives of these Gabor coefficients obtained from each parallel pipelined linear rf-processing components are then input to a least square error fitting means, a systolic array comprising a QR decomposition means and a backward substitution means. The output signal from the least square error fitting means then be used to updating of the affine parameters until the difference signals between the Gabor coefficients from the static and dynamical Gabor receptive fields are substantially reduced.

Description

[0001] 1. Field of the Invention[0002] The invention relates generally to an image analysis system and method for matching images and; more particularly, to massively parallel and pipelined arrays of processing elements for determining parameters of affine transformations between two images. The invention further relates to an image registration system comprising a plurality of specially constructed pipelined array of computing devices for real-time extraction of Gabor coefficients of windowed images and their affine Lie derivatives, and a system and method for determining the affine transformations among the windowed images over a designated scene spot.[0003] 2. Description of the Related Art[0004] Matching optical images taken on same scene surface area has many applications. Matching of two images means calculating from place to place the appropriate geometric transformations that compensate the differences of images. Image matching is an essential part and the hard core of vario...

AI Technical Summary

Benefits of technology

[0018] Accordingly, it is an object of this invention to provide a method and apparatus for rapidly determining the affine transformation that minimizes the differences between two windowed images, thereby matching these two windowed images with high accuracy.

Problems solved by technology

The method confronts with combinatorial complexity and is difficult to achieve real-time performance.

The computational problems from these application domains require high performance computers.

The computational problems involved in vision processes such as object recognition, target tracking, stereo image matching are much more sophisticated and difficult.

However, these problems also exhibit essential data parallelism, regularity, and pipelined data dependency.

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